Complex-Valued
Neural
Networks
have
higher
functionality,
learn
faster
and
generalize
better
than
their
real-valued
counterparts.
This
book
is
devoted
to
the
Multi-Valued
Neuron
(MVN)
and
MVN-based
neural
networks.
It
contains
a
comprehensive
observation
of
MVN
theory,
its
learning,
and
applications.
MVN
is
a
complex-valued
neuron
whose
inputs
and
output
are
located
on
the
unit
circle.
Its
activation
function
is
a
function
only
of
argument
(phase)
of
the
weighted
sum.
MVN
derivative-free
learning
is
based
on
the
error-correction
rule.
A
single
MVN
can
learn
those
input/output
mappings
that
are
non-linearly
separable
in
the
real
domain.
Such
classical
non-linearly
separable
problems
as
XOR
and
Parity
n
are
the
simplest
that
can
be
learned
by
a
single
MVN.
Another
important
advantage
of
MVN
is
a
proper
treatment
of
the
phase
information.
These
properties
of
MVN
become
even
more
remarkable
when
this
neuron
is
used
as
a
basic
one
in
neural
networks.
The
Multilayer
Neural
Network
based
on
Multi-Valued
Neurons
(MLMVN)
is
an
MVN-based
feedforward
neural
network.
Its
backpropagation
learning
algorithm
is
derivative-free
and
based
on
the
error-correction
rule.
It
does
not
suffer
from
the
local
minima
phenomenon.
MLMVN
outperforms
many
other
machine
learning
techniques
in
terms
of
learning
speed,
network
complexity
and
generalization
capability
when
solving
both
benchmark
and
real-world
classification
and
prediction
problems.
Another
interesting
application
of
MVN
is
its
use
as
a
basic
neuron
in
multi-state
associative
memories.
The
book
is
addressed
to
those
readers
who
develop
theoretical
fundamentals
of
neural
networks
and
use
neural
networks
for
solving
various
real-world
problems.
It
should
also
be
very
suitable
for
Ph.D.
and
graduate
students
pursuing
their
degrees
in
computational
intelligence.
Textul de pe ultima coperta
Complex-Valued
Neural
Networks
have
higher
functionality,
learn
faster
and
generalize
better
than
their
real-valued
counterparts.
This
book
is
devoted
to
the
Multi-Valued
Neuron
(MVN)
and
MVN-based
neural
networks.
It
contains
a
comprehensive
observation
of
MVN
theory,
its
learning,
and
applications.
MVN
is
a
complex-valued
neuron
whose
inputs
and
output
are
located
on
the
unit
circle.
Its
activation
function
is
a
function
only
of
argument
(phase)
of
the
weighted
sum.
MVN
derivative-free
learning
is
based
on
the
error-correction
rule.
A
single
MVN
can
learn
those
input/output
mappings
that
are
non-linearly
separable
in
the
real
domain.
Such
classical
non-linearly
separable
problems
as
XOR
and
Parity
n
are
the
simplest
that
can
be
learned
by
a
single
MVN.
Another
important
advantage
of
MVN
is
a
proper
treatment
of
the
phase
information.
These
properties
of
MVN
become
even
more
remarkable
when
this
neuron
is
used
as
a
basic
one
in
neural
networks.
The
Multilayer
Neural
Network
based
on
Multi-Valued
Neurons
(MLMVN)
is
an
MVN-based
feedforward
neural
network.
Its
backpropagation
learning
algorithm
is
derivative-free
and
based
on
the
error-correction
rule.
It
does
not
suffer
from
the
local
minima
phenomenon.
MLMVN
outperforms
many
other
machine
learning
techniques
in
terms
of
learning
speed,
network
complexity
and
generalization
capability
when
solving
both
benchmark
and
real-world
classification
and
prediction
problems.
Another
interesting
application
of
MVN
is
its
use
as
a
basic
neuron
in
multi-state
associative
memories.
The
book
is
addressed
to
those
readers
who
develop
theoretical
fundamentals
of
neural
networks
and
use
neural
networks
for
solving
various
real-world
problems.
It
should
also
be
very
suitable
for
Ph.D.
and
graduate
students
pursuing
their
degrees
in
computational
intelligence.
